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Group Conformity in Social Networks

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Abstract

Diffusion in social networks is a result of agents’ natural desires to conform to the behavioral patterns of their peers. In this article we show that the recently proposed “propositional opinion diffusion model” could be used to model an agent’s conformity to different social groups that the same agent might belong to, rather than conformity to the society as whole. The main technical contribution of this article is a sound and complete logical system describing the properties of the influence relation in this model. The logical system is an extension of Armstrong’s axioms from database theory by one new axiom that captures the topological structure of the network.

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Notes

  1. Boolean function \(f(x_1,\ldots ,x_n)\) is monotonic if \(f(x_1,\ldots ,x_n)\le f(y_1,\ldots ,y_n)\) for each all Boolean (0 or 1) values \(x_1\le y_1,x_2\le y_2,\ldots ,x_n\le y_n\).

  2. For the sake of completeness we prove this claim in Lemma 20 located in the appendix to this article.

References

  • Apt, K. R., & Markakis, E. (2011). Diffusion in social networks with competing products. In G. Persiano (Ed.), SAGT 2011, LNCS (Vol. 6982, pp. 212–223). Berlin: Springer.

    Google Scholar 

  • Armstrong, W. W. (1974). Dependency structures of data base relationships. In Information processing 74 (proc. IFIP congress, Stockholm, 1974) (pp. 580–583). Amsterdam, North-Holland.

  • Azimipour, S., & Naumov, P. (2016). Lighthouse principle for diffusion in social networks. arXiv preprint arXiv:1601.04098.

  • Baltag, A., Christoff, Z., Rendsvig, R. K., Smets, S. (2016). Dynamic epistemic logics of diffusion and prediction in social networks. In 12th conference on logic and the foundations of game and decision theory (LOFT), Maastricht, The Netherlands.

  • Biddle, B. J. (2013). Role theory: Expectations, identities, and behaviors. Cambridge: Academic Press.

    Google Scholar 

  • Christoff, Z., & Hansen, J. U. (2015). A logic for diffusion in social networks. Journal of Applied Logic, 13(1), 48–77.

    Article  Google Scholar 

  • Christoff, Z., & Rendsvig, R. K. (2014). Dynamic logics for threshold models and their epistemic extension. In Epistemic logic for individual, social, and interactive epistemology workshop.

  • Garcia-Molina, H., Ullman, J., & Widom, J. (2009). Database systems: The complete book (2nd ed.). Upper Saddle River: Prentice-Hall.

    Google Scholar 

  • Goyal, S. (1996). Interaction structure and social change. Journal of Institutional and Theoretical Economics, 152(3), 472–494.

    Google Scholar 

  • Grandi, U., Lorini, E., & Perrussel, L. (2015). Propositional opinion diffusion. In Proceedings of the 2015 international conference on autonomous agents and multiagent systems, international foundation for autonomous agents and multiagent systems (pp. 989–997).

  • Granovetter, M. (1978). Threshold models of collective behavior. American Journal of Sociology, 83(6), 1420–1443.

    Article  Google Scholar 

  • Hyman, H. H. (1942). The psychology of status. In Archives of psychology (Vol. 269). Columbia University.

  • Kelley, H. H., et al. (1952). Two functions of reference groups. Readings in Social Psychology, 2, 410–414.

    Google Scholar 

  • Kempe, D., Kleinberg, J., & Tardos, E. (2003). Maximizing the spread of influence through a social network. In KDD 03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 137–146). NY, USA: ACM. http://dl.acm.org/citation.cfm?id=956769

  • Li, F. H., Li, C. T., & Shan, M. K. (2011). Labeled influence maximization in social networks for target marketing. In 2011 IEEE third international conference on privacy, security, risk and trust and 2011 IEEE third international conference on social computing (pp. 560–563). https://doi.org/10.1109/PASSAT/SocialCom.2011.152. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6113168.

  • Liu, F., Seligman, J., & Girard, P. (2014). Logical dynamics of belief change in the community. Synthese, 191(11), 2403–2431.

    Article  Google Scholar 

  • Morris, S. (2000). Contagion. Review of Economic Studies, 67, 57–78.

    Article  Google Scholar 

  • Naumov, P., & Tao, J. (2016). Marketing impact on diffusion in social networks. In 12th conference on logic and the foundations of game and decision theory (LOFT), Maastricht, The Netherlands.

  • Naumov, P., & Tao, J. (2017). Marketing impact on diffusion in social networks. Journal of Applied Logic, 20, 49–74.

    Article  Google Scholar 

  • Schelling, T. C. (1969). Models of segregation. The American Economic Review, 59(2), 488–493.

    Google Scholar 

  • Seligman, J., Liu, F., & Girard, P. (2011). Logic in the community. In M. Banerjee & A. Seth (Eds.), Logic and its applications (pp. 178–188). Berlin: Springer.

  • Seligman, J., Liu, F., & Girard, P. (2013). Facebook and the epistemic logic of friendship. In 14th conference on theoretical aspects of rationality and knowledge (TARK ‘13), January 2013, (pp. 229–238). Chennai, India.

  • Shibutani, T. (1955). Reference groups as perspectives. American Journal of Sociology, 60(6), 562–569.

    Article  Google Scholar 

  • Tajfel, H. (1979). Individuals and groups in social psychology. British Journal of Clinical Psychology, 18(2), 183–190.

    Article  Google Scholar 

  • Turner, R. H. (1956). Role-taking, role standpoint, and reference-group behavior. American Journal of Sociology, 61(4), 316–328.

    Article  Google Scholar 

  • Xiong, Z., Ågotnes, T., Seligman, J., & Zhu, R. (2017). Towards a logic of tweeting. In A. Baltag, J. Seligman, & T. Yamada (Eds.), Logic, rationality, and interaction (pp. 49–64). Berlin, Heidelberg: Springer.

    Chapter  Google Scholar 

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Monotonic Boolean Functions

Monotonic Boolean Functions

In this appendix we prove the property of monotonic Boolean functions that we referred to in the introduction.

Lemma 20

Any monotonic Boolean formula can be written as a disjunction of several disjuncts where each disjunct is a conjunction of several propositional variables.

Proof

Consider any monotonic Boolean formula \(\varphi (x_1,\ldots ,x_n)\). If \(b_1,\ldots ,b_n\) are Boolean values, then by \( \varphi \left( \begin{array}{ccccc} 1 &{} 2 &{} 3 &{} \cdots &{} n \\ b_1 &{} b_2 &{} b_3 &{} \cdots &{} b_n \end{array}\right) \) we denote the Boolean value of formula \(\varphi \) on Boolean arguments \(b_1,\ldots ,b_n\). Consider Boolean expression

$$\begin{aligned} \psi = \bigvee \left\{ x_{i_1}\wedge x_{i_2}\wedge \dots \wedge x_{i_k}\;\big |\; \varphi \left( \begin{array}{ccccccccccc} 1 &{} \dots &{} i_1-1 &{} i_1 &{} i_1+1 &{} \dots &{} i_k-1 &{} i_k &{} i_k+1 &{} \dots &{} n\\ 0 &{} \dots &{} 0 &{} 1 &{} 0 &{} \cdots &{} 0 &{} 1 &{} 0 &{} \dots &{} 0 \end{array}\right) = 1 \right\} . \end{aligned}$$
(2)

Next we show that formulae \(\varphi \) and \(\psi \) are equivalent. Consider any Boolean values \(b_1,\ldots ,b_n\). We will show that \( \varphi \left( \begin{array}{ccccc} 1 &{} 2 &{} 3 &{} \cdots &{} n \\ b_1 &{} b_2 &{} b_3 &{} \cdots &{} b_n \end{array}\right) = 1 \) if and only if \( \psi \left( \begin{array}{ccccc} 1 &{} 2 &{} 3 &{} \cdots &{} n \\ b_1 &{} b_2 &{} b_3 &{} \cdots &{} b_n \end{array}\right) =1 \)

\((\Rightarrow )\) Let \(i_1,\ldots ,i_n\) be all indices i such that \(b_i=1\). Then,

$$\begin{aligned} \varphi \left( \begin{array}{ccccccccccc} 1 &{} \dots &{} i_1-1 &{} i_1 &{} i_1+1 &{} \dots &{} i_k-1 &{} i_k &{} i_k+1 &{} \dots &{} n\\ 0 &{} \dots &{} 0 &{} 1 &{} 0 &{} \cdots &{} 0 &{} 1 &{} 0 &{} \dots &{} 0 \end{array}\right) = 1. \end{aligned}$$

Hence, conjunction \(x_{i_1}\wedge x_{i_2}\wedge \dots \wedge x_{i_k}\) is one of disjuncts in formula \(\psi \). Note that

$$\begin{aligned} (x_{i_1}\wedge x_{i_2}\wedge \dots \wedge x_{i_k}) \left( \begin{array}{ccccccccccc} 1 &{} \dots &{} i_1-1 &{} i_1 &{} i_1+1 &{} \dots &{} i_k-1 &{} i_k &{} i_k+1 &{} \dots &{} n\\ 0 &{} \dots &{} 0 &{} 1 &{} 0 &{} \cdots &{} 0 &{} 1 &{} 0 &{} \dots &{} 0 \end{array}\right) = 1\wedge \dots \wedge 1=1. \end{aligned}$$

Therefore,

$$\begin{aligned} \psi \left( \begin{array}{ccccccccccc} 1 &{} \dots &{} i_1-1 &{} i_1 &{} i_1+1 &{} \dots &{} i_k-1 &{} i_k &{} i_k+1 &{} \dots &{} n\\ 0 &{} \dots &{} 0 &{} 1 &{} 0 &{} \cdots &{} 0 &{} 1 &{} 0 &{} \dots &{} 0 \end{array}\right) = 1. \end{aligned}$$

because conjunction \(x_{i_1}\wedge x_{i_2}\wedge \dots \wedge x_{i_k}\) is one of disjuncts in formula \(\psi \).

\((\Leftarrow )\) In order for a disjunction to have value 1 at least one disjunct must have value 1. Thus, assumption \( \psi \left( \begin{array}{ccccc} 1 &{} 2 &{} 3 &{} \cdots &{} n \\ b_1 &{} b_2 &{} b_3 &{} \cdots &{} b_n \end{array}\right) =1 \) and Eq. (2) imply that there are indices \(i_1,\ldots ,i_k\) such that

$$\begin{aligned} (x_{i_1}\wedge x_{i_2}\wedge \dots \wedge x_{i_k})\left( \begin{array}{ccccc} 1 &{} 2 &{} 3 &{} \cdots &{} n \\ b_1 &{} b_2 &{} b_3 &{} \cdots &{} b_n \end{array}\right) =1 \end{aligned}$$
(3)

and

$$\begin{aligned} \varphi \left( \begin{array}{ccccccccccc} 1 &{} \dots &{} i_1-1 &{} i_1 &{} i_1+1 &{} \dots &{} i_k-1 &{} i_k &{} i_k+1 &{} \dots &{} n\\ 0 &{} \dots &{} 0 &{} 1 &{} 0 &{} \cdots &{} 0 &{} 1 &{} 0 &{} \dots &{} 0 \end{array}\right) = 1. \end{aligned}$$
(4)

Note that \( (x_{i_1}\wedge x_{i_2}\wedge \dots \wedge x_{i_k})\left( \begin{array}{ccccc} 1 &{} 2 &{} 3 &{} \cdots &{} n \\ b_1 &{} b_2 &{} b_3 &{} \cdots &{} b_n \end{array}\right) = b_{i_1}\wedge \dots \wedge b_{i_k} \). Thus, equality (3) implies that \(b_{i_1}=\dots =b_{i_k}=1\). Hence, it follows from Eq. (4) that

$$\begin{aligned} \varphi \left( \begin{array}{ccccccccccc} 1 &{} \dots &{} i_1-1 &{} i_1 &{} i_1+1 &{} \dots &{} i_k-1 &{} i_k &{} i_k+1 &{} \dots &{} n\\ 0 &{} \dots &{} 0 &{} b_i &{} 0 &{} \cdots &{} 0 &{} b_k &{} 0 &{} \dots &{} 0 \end{array}\right) = 1. \end{aligned}$$

Therefore,

$$\begin{aligned} \varphi \left( \begin{array}{ccccccccccc} 1 &{} \dots &{} i_1-1 &{} i_1 &{} i_1+1 &{} \dots &{} i_k-1 &{} i_k &{} i_k+1 &{} \dots &{} n\\ b_1 &{} \dots &{} b_{i-1} &{} b_i &{} b_{i+1} &{} \cdots &{} b_{k-1} &{} b_k &{} b_{k+1} &{} \dots &{} b_n \end{array}\right) = 1. \end{aligned}$$

due to the assumption that formula \(\varphi \) is monotonic. \(\square \)

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Morrison, C., Naumov, P. Group Conformity in Social Networks. J of Log Lang and Inf 29, 3–19 (2020). https://doi.org/10.1007/s10849-019-09303-5

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